Papers
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
Ariel Jaffe, Roi Weiss, Boaz Nadler et al.
Anonymous Walk Embeddings
Sergey Ivanov, Evgeny Burnaev
Unbiased Objective Estimation in Predictive Optimization
Shinji Ito, Akihiro Yabe, Ryohei Fujimaki
Deep Density Destructors
David Inouye, Pradeep Ravikumar
Improving Regression Performance with Distributional Losses
Ehsan Imani, Martha White
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
Hideaki Imamura, Issei Sato, Masashi Sugiyama
Black-box Adversarial Attacks with Limited Queries and Information
Andrew Ilyas, Logan Engstrom, Anish Athalye et al.
Attention-based Deep Multiple Instance Learning
Maximilian Ilse, Jakub Tomczak, Max Welling
Deep Variational Reinforcement Learning for POMDPs
Maximilian Igl, Luisa Zintgraf, Tuan Anh Le et al.
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning
Rodrigo Toro Icarte, Toryn Klassen, Richard Valenzano et al.
Decoupled Parallel Backpropagation with Convergence Guarantee
Zhouyuan Huo, Bin Gu, Yang et al.
Topological mixture estimation
Steve Huntsman
Neural Autoregressive Flows
Chin-Wei Huang, David Krueger, Alexandre Lacoste et al.
Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling
Kejun Huang, Xiao Fu, Nicholas Sidiropoulos
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Furong Huang, Jordan Ash, John Langford et al.
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
Bin Hu, Stephen Wright, Laurent Lessard
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
Weihua Hu, Gang Niu, Issei Sato et al.
Variational Bayesian dropout: pitfalls and fixes
Jiri Hron, Alex Matthews, Zoubin Ghahramani
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks
Mingyi Hong, Meisam Razaviyayn, Jason Lee
Sound Abstraction and Decomposition of Probabilistic Programs
Steven Holtzen, Guy Broeck, Todd Millstein
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Judy Hoffman, Eric Tzeng, Taesung Park et al.
Fast Bellman Updates for Robust MDPs
Chin Pang Ho, Marek Petrik, Wolfram Wiesemann
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
Kyle Helfrich, Devin Willmott, Qiang Ye
Learning unknown ODE models with Gaussian processes
Markus Heinonen, Cagatay Yildiz, Henrik Mannerström et al.